{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/markramirez/Public/Dropbox/NOAA STUDY KRVZ/Data/BJPS Replication Files/BJPSlogfileBinary.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}24 Jul 2016, 07:21:07

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. 
. use "/Users/markramirez/Public/Dropbox/NOAA STUDY KRVZ/Data/BJPS Replication Files/noaasammy.dta"
{txt}
{com}. 
. ********************************************************
. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. 
. ********************************************************
. ///* DEMOGRAPHIC VARIABLES AND POLITICAL ORIENTATION*///
> ********************************************************
. ** generate id variable 
. gen id = _n
{txt}
{com}. 
. /*RACE*/
. /*white = 1 non-white = 0 */
. /*Lose 37 cases */
. rename q119fin1 race
{txt}
{com}. recode race 1=0 2=1 3=0 4=0 5=0 100=0 101=0 102=0 103=0
{txt}(race: 1056 changes made)

{com}. label drop q119fin1
{txt}
{com}. label define q119fin1 0 "non-white" 1 "white"
{txt}
{com}. tab race

       {txt}race {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
  non-white {c |}{res}        168       15.91       15.91
{txt}      white {c |}{res}        888       84.09      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,056      100.00
{txt}
{com}. 
. /*EDUCATION*/
. /*Less than high school = 0 Post-Graduate = 1*/
. rename q116 education
{txt}
{com}. recode education 3=2 4=3 5=4 6=5
{txt}(education: 838 changes made)

{com}. label drop q116
{txt}
{com}. label define q116 1 "some high school"2 "high school/vocational" 3 "some college"  4 "college" 5 "post-graduate" 
{txt}
{com}. codebook education 

{txt}{hline}
{res}education{right:education}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:q116}

{col 18}range:  [{res}1{txt},{res}5{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}5{col 51}{txt}missing .:  {res}12{txt}/{res}1093

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}     22{col 33}       1{col 43}{txt}some high school
{col 24}{res}    241{col 33}       2{col 43}{txt}high school/vocational
{col 24}{res}    303{col 33}       3{col 43}{txt}some college
{col 24}{res}    331{col 33}       4{col 43}{txt}college
{col 24}{res}    184{col 33}       5{col 43}{txt}post-graduate
{col 24}{res}     12{col 33}       .{col 43}
{txt}
{com}. 
. /*INCOME*/
. /*Lose 244 cases*/
. rename q122 income
{txt}
{com}. 
. /*AGE*/
. /*Ranges from 18 to 90*/
. /*Lose 32 cases*/
. rename q117 age
{txt}
{com}. 
. /*RELIGIOUS ATTENDANCE*/
. /* Attendend = 1 Not attend = 0*/
. /* lose 16 cases*/
. rename q124 attendance
{txt}
{com}. recode attendance 2=0
{txt}(attendance: 0 changes made)

{com}. 
. /*IDEOLOGY*/
. /* Lose 134 cases*/
. rename q118 ideo
{txt}
{com}. 
. /*PARTISANSHIP*/
. /* Lose 46 cases*/
. rename q114 pid
{txt}
{com}. 
. ** recode democrat = 1 republican = -1
. recode pid 2 = 0 3 = 1 1 = 2 8 = 1 9 = 1 
{txt}(pid: 1093 changes made)

{com}. label drop q114
{txt}
{com}. label define q114 0 "republican" 1 "independent/else" 2 "democrat"  
{txt}
{com}. codebook pid 

{txt}{hline}
{res}pid{right:suppose you were in the voting booth and you came across an office for which two}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:q114}

{col 18}range:  [{res}0{txt},{res}2{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}3{col 51}{txt}missing .:  {res}0{txt}/{res}1093

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}    256{col 33}       0{col 43}{txt}republican
{col 24}{res}    480{col 33}       1{col 43}{txt}independent/else
{col 24}{res}    357{col 33}       2{col 43}{txt}democrat

{com}. 
. /*EFFICACY*/
. /*Higher values are associated with lower efficacy*/
. /*Lose 102 cases*/
. cor q71 q73 q74
{txt}(obs=991)

             {c |}      q71      q73      q74
{hline 13}{c +}{hline 27}
         q71 {c |}{res}   1.0000
         {txt}q73 {c |}{res}   0.3621   1.0000
         {txt}q74 {c |}{res}   0.4882   0.2457   1.0000

{txt}
{com}. factor q71 q73 q74
{txt}(obs=991)

Factor analysis/correlation{col 52}Number of obs    = {res}     991
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       1
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       3

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.98296      1.04220            1.4417       1.4417
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}     -0.05924      0.18268           -0.0869       1.3548
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.24192            .           -0.3548       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}3{txt})  ={res}  415.39{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q71}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6594}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5652}}}{space 1}
{space 4}{space 0}{ralign 12:q73}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4526}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7952}}}{space 1}
{space 4}{space 0}{ralign 12:q74}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5859}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6567}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. alpha q71 q73 q74, detail gen (efficacy)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .1693972
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.6338

{txt}Interitem covariances (obs=pairwise, see below)

        q71     q73     q74
q71  {res}0.5078
{txt}q73  {res}0.1624  0.4029
{txt}q74  {res}0.2380  0.1055  0.4767

{txt}Pairwise number of observations

      q71   q73   q74
q71  {res}1073
{txt}q73  {res}1023  1035
{txt}q74  {res}1033   999  1045
{txt}
{com}. 
. /*RISK PERCEPTIONS*/
. /*Higher values are associated with higher risk perception */ 
. recode q101 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q101: 1037 changes made)

{com}. recode q102 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q102: 1014 changes made)

{com}. recode q103 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q103: 1031 changes made)

{com}. factor q101 q102 q103 
{txt}(obs=980)

Factor analysis/correlation{col 52}Number of obs    = {res}     980
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       1
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       3

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.77400      1.89251            1.1834       1.1834
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}     -0.11851      0.03796           -0.0791       1.1044
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.15648            .           -0.1044       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}3{txt})  ={res} 1191.31{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q101}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7924}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3720}}}{space 1}
{space 4}{space 0}{ralign 12:q102}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7273}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4710}}}{space 1}
{space 4}{space 0}{ralign 12:q103}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7855}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3829}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. alpha q101 q102 q103, detail gen (risk)  

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res}  .040741
{txt}Number of items in the scale:{col 34}{res}        3
{txt}Scale reliability coefficient:{col 34}{res}   0.8428

{txt}Interitem covariances (obs=pairwise, see below)

        q101    q102    q103
q101  {res}0.0722
{txt}q102  {res}0.0407  0.0592
{txt}q103  {res}0.0450  0.0364  0.0591

{txt}Pairwise number of observations

      q101  q102  q103
q101  {res}1037
{txt}q102  {res} 998  1014
{txt}q103  {res}1007   993  1031
{txt}
{com}. 
. /*NETWORK INTEREST*/
. /*Higher values associated with greater network interest*/
. recode q81 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q81: 1087 changes made)

{com}. recode q82 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q82: 1087 changes made)

{com}. factor q81 q82 q83 q85
{txt}(obs=1080)

Factor analysis/correlation{col 52}Number of obs    = {res}    1080
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.81814      1.53358            1.0636       1.0636
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.28456      0.47109            0.1665       1.2301
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.18653      0.02025           -0.1091       1.1210
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.20678            .           -0.1210       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res} 1382.63{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q81}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7265}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2610}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4041}}}{space 1}
{space 4}{space 0}{ralign 12:q82}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7721}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2041}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3622}}}{space 1}
{space 4}{space 0}{ralign 12:q83}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6111}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2818}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5472}}}{space 1}
{space 4}{space 0}{ralign 12:q85}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5664}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3089}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5838}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. alpha q81 q82 q83 q85, detail gen (network)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0729956
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.7462

{txt}Interitem covariances (obs=pairwise, see below)

        q81     q82     q83     q85
q81  {res}0.1045
{txt}q82  {res}0.0727  0.1010
{txt}q83  {res}0.0594  0.0667  0.2449
{txt}q85  {res}0.0501  0.0593  0.1296  0.2390

{txt}Pairwise number of observations

      q81   q82   q83   q85
q81  {res}1087
{txt}q82  {res}1086  1087
{txt}q83  {res}1086  1086  1089
{txt}q85  {res}1081  1081  1084  1084
{txt}
{com}. 
. 
. ** create ideological strength 
. gen strength_ideo = 0 
{txt}
{com}. recode strength_ideo 0 = 3 if ideo == 1
{txt}(strength_ideo: 58 changes made)

{com}. recode strength_ideo 0 = 3 if ideo == 7
{txt}(strength_ideo: 119 changes made)

{com}. recode strength_ideo 0 = 2 if ideo == 6
{txt}(strength_ideo: 180 changes made)

{com}. recode strength_ideo 0 = 2 if ideo == 2
{txt}(strength_ideo: 179 changes made)

{com}. recode strength_ideo 0 = 1 if ideo == 3
{txt}(strength_ideo: 108 changes made)

{com}. recode strength_ideo 0 = 1 if ideo == 5
{txt}(strength_ideo: 145 changes made)

{com}. 
. 
. ************************************
. ///* INDICATORS OF INFORMATION *///
> ************************************
. /* SCIENTIFIC INFORMATION 1 */
. /* 1 correct, 0 wrong */
. /* Lose 13 cases */
. cor q12 q13 q14 q15
{txt}(obs=1080)

             {c |}      q12      q13      q14      q15
{hline 13}{c +}{hline 36}
         q12 {c |}{res}   1.0000
         {txt}q13 {c |}{res}   0.0521   1.0000
         {txt}q14 {c |}{res}   0.2260   0.0802   1.0000
         {txt}q15 {c |}{res}   0.2196   0.0005   0.1004   1.0000

{txt}
{com}. factor q12 q13 q14 q15 
{txt}(obs=1080)

Factor analysis/correlation{col 52}Number of obs    = {res}    1080
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.45391      0.42155            2.1711       2.1711
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.03236      0.11624            0.1548       2.3259
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.08388      0.10945           -0.4012       1.9247
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.19332            .           -0.9247       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res}  121.36{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q12}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4447}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0189}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8019}}}{space 1}
{space 4}{space 0}{ralign 12:q13}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1176}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1449}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9652}}}{space 1}
{space 4}{space 0}{ralign 12:q14}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3625}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0571}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8653}}}{space 1}
{space 4}{space 0}{ralign 12:q15}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3330}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0881}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8814}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. alpha q12 q13 q14 q15, detail gen (sci_obknowledge)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0240184
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.3552

{txt}Interitem covariances (obs=pairwise, see below)

        q12     q13     q14     q15
q12  {res}0.2477
{txt}q13  {res}0.0096  0.1255
{txt}q14  {res}0.0473  0.0116  0.1730
{txt}q15  {res}0.0543  0.0005  0.0208  0.2477

{txt}Pairwise number of observations

      q12   q13   q14   q15
q12  {res}1088
{txt}q13  {res}1086  1088
{txt}q14  {res}1087  1087  1089
{txt}q15  {res}1082  1082  1084  1085
{txt}
{com}. 
. /* Domain-Specific Knowldge of GW "causes" */
. cor q62 q63 q66 q67
{txt}(obs=1085)

             {c |}      q62      q63      q66      q67
{hline 13}{c +}{hline 36}
         q62 {c |}{res}   1.0000
         {txt}q63 {c |}{res}   0.1959   1.0000
         {txt}q66 {c |}{res}   0.1202   0.0779   1.0000
         {txt}q67 {c |}{res}   0.0701  -0.0175   0.0895   1.0000

{txt}
{com}. factor q62 q63 q66 q67  
{txt}(obs=1085)

Factor analysis/correlation{col 52}Number of obs    = {res}    1085
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.33173      0.27333            2.4835       2.4835
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.05840      0.14591            0.4372       2.9207
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.08752      0.08152           -0.6552       2.2655
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.16904            .           -1.2655       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res}   75.58{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q62}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3742}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0304}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8591}}}{space 1}
{space 4}{space 0}{ralign 12:q63}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3171}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1234}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8842}}}{space 1}
{space 4}{space 0}{ralign 12:q66}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2675}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0928}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9198}}}{space 1}
{space 4}{space 0}{ralign 12:q67}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1400}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1834}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.9468}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. alpha q62 q63 q66 q67,detail gen (gw_know)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0201437
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.2859

{txt}Interitem covariances (obs=pairwise, see below)

         q62      q63      q66      q67
q62  {res} 0.2407
{txt}q63  {res} 0.0436   0.2077
{txt}q66  {res} 0.0293   0.0172   0.2465
{txt}q67  {res} 0.0149  -0.0034   0.0193   0.1905

{txt}Pairwise number of observations

      q62   q63   q66   q67
q62  {res}1088
{txt}q63  {res}1088  1089
{txt}q66  {res}1085  1086  1086
{txt}q67  {res}1086  1087  1086  1087
{txt}
{com}. 
. /* Domain-specific by partisans and ideology */ 
. alpha q62 q63 q66 q67 if pid==0, detail

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0201991
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.2817

{txt}Interitem covariances (obs=pairwise, see below)

         q62      q63      q66      q67
q62  {res} 0.2334
{txt}q63  {res} 0.0440   0.2301
{txt}q66  {res} 0.0134   0.0399   0.2419
{txt}q67  {res} 0.0141  -0.0145   0.0242   0.1996

{txt}Pairwise number of observations

     q62  q63  q66  q67
q62  {res}253
{txt}q63  {res}253  253
{txt}q66  {res}252  252  252
{txt}q67  {res}252  252  252  252
{txt}
{com}. alpha q62 q63 q66 q67 if pid==2, detail

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0186036
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.2793

{txt}Interitem covariances (obs=pairwise, see below)

        q62     q63     q66     q67
q62  {res}0.2434
{txt}q63  {res}0.0349  0.1789
{txt}q66  {res}0.0375  0.0057  0.2507
{txt}q67  {res}0.0143  0.0025  0.0167  0.1696

{txt}Pairwise number of observations

     q62  q63  q66  q67
q62  {res}357
{txt}q63  {res}357  357
{txt}q66  {res}356  356  356
{txt}q67  {res}357  357  356  357
{txt}
{com}. 
. alpha q62 q63 q66 q67 if ideo==1, detail

{txt}Test scale = mean(unstandardized items)
Reversed item: {res: q63}

Average interitem covariance:{col 34}{res}  .015729
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.2470

{txt}Interitem covariances (reverse applied) (obs=58 in all pairs)

         q62      q63      q66      q67
q62  {res} 0.2396
{txt}q63  {res}-0.0163   0.1770
{txt}q66  {res} 0.0333   0.0284   0.2468
{txt}q67  {res} 0.0079   0.0230   0.0181   0.1670
{txt}
{com}. alpha q62 q63 q66 q67 if ideo==7, detail

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0343651
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.4037

{txt}Interitem covariances (obs=pairwise, see below)

        q62     q63     q66     q67
q62  {res}0.2241
{txt}q63  {res}0.0546  0.2495
{txt}q66  {res}0.0074  0.0527  0.2490
{txt}q67  {res}0.0309  0.0067  0.0536  0.2269

{txt}Pairwise number of observations

     q62  q63  q66  q67
q62  {res}117
{txt}q63  {res}117  118
{txt}q66  {res}116  117  117
{txt}q67  {res}116  117  117  117
{txt}
{com}. 
. 
. ** Descriptive analysis  
. ** Correlation between pid ideo and gw_know education 
. spearman gw_know education

{txt} Number of obs = {res}   1077
{txt}Spearman's rho = {res}     -0.0036

{txt}Test of Ho: gw_know and education are independent
    Prob > |t| = {res}      0.9070
{txt}
{com}. spearman gw_know ideo

{txt} Number of obs = {res}   1044
{txt}Spearman's rho = {res}     -0.0695

{txt}Test of Ho: gw_know and ideo are independent
    Prob > |t| = {res}      0.0247
{txt}
{com}. spearman gw_know pid

{txt} Number of obs = {res}   1089
{txt}Spearman's rho = {res}      0.0650

{txt}Test of Ho: gw_know and pid are independent
    Prob > |t| = {res}      0.0320
{txt}
{com}. spearman ideo pid

{txt} Number of obs = {res}   1047
{txt}Spearman's rho = {res}     -0.5449

{txt}Test of Ho: ideo and pid are independent
    Prob > |t| = {res}      0.0000
{txt}
{com}. spearman gw_know sci_obknowledge

{txt} Number of obs = {res}   1088
{txt}Spearman's rho = {res}      0.1123

{txt}Test of Ho: gw_know and sci_obknowledge are independent
    Prob > |t| = {res}      0.0002
{txt}
{com}. 
. *********************************************
. ///* 1 - |NEP - HEP| CORE VALUE CONFLICT *///
> *********************************************
. /*Recode HEP and NEP components to 0 to 1 scale with higher values indicating pro-enviornmental/human paradigm*/
. recode q20 4 = 1 3 = .67 2 = .33 1 = 0 
{txt}(q20: 1063 changes made)

{com}. recode q21 4 = 1 3 = .67 2 = .33 1 = 0 
{txt}(q21: 1057 changes made)

{com}. recode q22 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q22: 1064 changes made)

{com}. recode q23 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q23: 1026 changes made)

{com}. recode q24 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q24: 1079 changes made)

{com}. recode q25 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q25: 1069 changes made)

{com}. recode q26 4 = 0 3 = .33 2 = .67
{txt}(q26: 1020 changes made)

{com}. recode q27 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q27: 1040 changes made)

{com}. recode q28 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q28: 1044 changes made)

{com}. recode q29 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q29: 1064 changes made)

{com}. recode q30 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q30: 1053 changes made)

{com}. recode q31 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q31: 1073 changes made)

{com}. recode q32 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q32: 1046 changes made)

{com}. recode q33 4 = 1 3 = .67 2 = .33 1 = 0
{txt}(q33: 1040 changes made)

{com}. 
. /*NEP*/
. /* Range 1 (high environmental concern) to 0 (environmental concern) */
. alpha q20 q22 q24 q29 q31 q33, detail gen (nep)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0202498
{txt}Number of items in the scale:{col 34}{res}        6
{txt}Scale reliability coefficient:{col 34}{res}   0.7804

{txt}Interitem covariances (obs=pairwise, see below)

        q20     q22     q24     q29     q31     q33
q20  {res}0.0745
{txt}q22  {res}0.0187  0.0540
{txt}q24  {res}0.0218  0.0249  0.0527
{txt}q29  {res}0.0243  0.0120  0.0145  0.0474
{txt}q31  {res}0.0172  0.0206  0.0207  0.0159  0.0437
{txt}q33  {res}0.0241  0.0229  0.0285  0.0162  0.0216  0.0545

{txt}Pairwise number of observations

      q20   q22   q24   q29   q31   q33
q20  {res}1063
{txt}q22  {res}1042  1064
{txt}q24  {res}1051  1052  1079
{txt}q29  {res}1041  1041  1052  1064
{txt}q31  {res}1048  1051  1061  1050  1073
{txt}q33  {res}1019  1023  1029  1016  1028  1040
{txt}
{com}. 
. ********************************************************
. ///* 1 - |NEP - ECONOMIC PRIORITIES| VALUE CONFLICT *///
> ********************************************************
. /* Higher values indicate greater support for economy*/
. recode q7 4=1 3=.67 2=.33 1=0
{txt}(q7: 1051 changes made)

{com}. recode q9 4=0 3=.33 2=.67 
{txt}(q9: 948 changes made)

{com}. alpha q7 q9, detail gen(economy)

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .0282268
{txt}Number of items in the scale:{col 34}{res}        2
{txt}Scale reliability coefficient:{col 34}{res}   0.5409

{txt}Interitem covariances (obs=pairwise, see below)

        q7      q9
q7  {res}0.0722
{txt}q9  {res}0.0282  0.0801

{txt}Pairwise number of observations

      q7    q9
q7  {res}1051
{txt}q9  {res}1021  1052
{txt}
{com}. gen nepvecon = 1 - abs(nep - economy)
{txt}(12 missing values generated)

{com}. 
. 
. ** Pluralism and interactions
. ** create value pluralism 
. gen pluralism = ((nep + economy)/2) - abs(nep - economy)
{txt}(12 missing values generated)

{com}. 
. ** create interaction of education and value pluralism
. gen educ_plural = education * pluralism 
{txt}(23 missing values generated)

{com}. 
. ** create interaction of global warming knowledge and value pluralism 
. gen  gwknow_plural = gw_know * pluralism 
{txt}(15 missing values generated)

{com}. 
. ** create interaction of partisanship and value pluralism 
. gen pid_plural = pid * pluralism 
{txt}(12 missing values generated)

{com}. 
. ** create interaction of partisanship and value pluralism 
. gen pid_gw_pluralism = pid * gwknow_plural
{txt}(15 missing values generated)

{com}. 
. 
. ***************************************************
. ///* CLIMATE CHANGE POLICY DEPENDENT VARIABLES *///
> ***************************************************
. /* In this area we create the dependent variables.  These are based upon policy preferences  */
. /* with regard to solving CC problems.  They are on a four-point scale */
. 
. /* First, combine q93_v1 and q93_v2, as they are conceptually the same, but differently worded.  This will */
. /* allow us to have a variable comparable in number of observations to the others */
. 
. recode q93_v1 .=0
{txt}(q93_v1: 595 changes made)

{com}. recode q93_v2 .=0
{txt}(q93_v2: 528 changes made)

{com}. gen q93 = q93_v1 + q93_v2
{txt}
{com}. /* Recode 0 in q93 to missing value so that it is not included */
. recode q93 0=.
{txt}(q93: 30 changes made)

{com}. 
. /* Second, rename the variables of policy preferences */
. 
. rename q89 emission
{txt}
{com}. rename q90 tax_industry
{txt}
{com}. rename q91 tax_individuals
{txt}
{com}. rename q92 educatepublic
{txt}
{com}. rename q93 setprice
{txt}
{com}. rename q94 kyoto
{txt}
{com}. rename q95 law
{txt}
{com}. rename q96 renewable
{txt}
{com}. rename q97 methane
{txt}
{com}. rename q98 seawalls
{txt}
{com}. rename q99 vehicle
{txt}
{com}. rename q100 gas
{txt}
{com}. 
. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. **********************
. ** FACTOR ANALYSIS 
. ***********************
. ** Outcome
. polychoric emission tax_industry tax_individuals educatepublic setprice kyoto law renewable methane seawalls vehicle gas
{res}
{txt}Polychoric correlation matrix

                        emission     tax_industry  tax_individuals    educatepublic         setprice
       emission  {res}              1
{txt}   tax_industry  {res}      .38294996                1
{txt}tax_individuals  {res}       .2817427        .65884569                1
{txt}  educatepublic  {res}      .42596698        .56156444        .45638559                1
{txt}       setprice  {res}      .38015571        .44741967        .39705273        .51586584                1
{txt}          kyoto  {res}      .28502789        .59065392        .56094995        .56813167         .5524393
{txt}            law  {res}      .24114177        .55109286         .5248511        .54684892        .44766774
{txt}      renewable  {res}      .31381857        .43031226        .29588303        .55078705        .44040616
{txt}        methane  {res}      .29683583        .51012867        .43641356        .50204919        .44330616
{txt}       seawalls  {res}      .19813302        .35309218        .32577301        .32791816        .24427951
{txt}        vehicle  {res}      .32556788        .53788939        .46732339        .58583267        .46884118
{txt}            gas  {res}      .19821008        .41686886        .53847394        .38795639        .34317912

                 {txt}          kyoto              law        renewable          methane         seawalls
          kyoto  {res}              1
{txt}            law  {res}        .662264                1
{txt}      renewable  {res}      .47118614        .43726816                1
{txt}        methane  {res}      .59207183         .4820782        .43115457                1
{txt}       seawalls  {res}      .30652183        .30504036        .26048449        .44731962                1
{txt}        vehicle  {res}      .60690978         .6719865        .51398762        .50619422        .30972162
{txt}            gas  {res}      .47119116        .45347512        .25060855        .38205406        .21223494

                 {txt}        vehicle              gas
        vehicle  {res}              1
{txt}            gas  {res}      .39370877                1
{txt}
{com}. display r(sum_w)
{res}804
{txt}
{com}. global N=r(sum_w)
{txt}
{com}. matrix r=r(R)
{txt}
{com}. factormat r, n($N) factors(2)
{txt}(obs=804)

Factor analysis/correlation{col 52}Number of obs    = {res}     804
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}      23

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      5.37100      4.94571            0.9655       0.9655
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.42529      0.17079            0.0765       1.0420
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.25450      0.07151            0.0458       1.0878
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.18299      0.10599            0.0329       1.1207
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}      0.07701      0.08507            0.0138       1.1345
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}     -0.00807      0.03842           -0.0015       1.1330
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}     -0.04649      0.04775           -0.0084       1.1247
{txt}{col 5}{ralign 11:Factor8}  {c |}{res}     -0.09424      0.01177           -0.0169       1.1077
{txt}{col 5}{ralign 11:Factor9}  {c |}{res}     -0.10600      0.03677           -0.0191       1.0887
{txt}{col 5}{ralign 11:Factor10}  {c |}{res}     -0.14278      0.01656           -0.0257       1.0630
{txt}{col 5}{ralign 11:Factor11}  {c |}{res}     -0.15934      0.03189           -0.0286       1.0344
{txt}{col 5}{ralign 11:Factor12}  {c |}{res}     -0.19123            .           -0.0344       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}66{txt}) ={res} 4484.24{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:emission}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4491}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2025}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7573}}}{space 1}
{space 4}{space 0}{ralign 12:tax_industry}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7585}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1233}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4095}}}{space 1}
{space 4}{space 0}{ralign 12:tax_indivi~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6943}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3522}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3939}}}{space 1}
{space 4}{space 0}{ralign 12:educatepub~c}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7497}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2001}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3980}}}{space 1}
{space 4}{space 0}{ralign 12:setprice}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6412}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1554}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5647}}}{space 1}
{space 4}{space 0}{ralign 12:kyoto}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8000}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0653}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3557}}}{space 1}
{space 4}{space 0}{ralign 12:law}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7549}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0911}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4218}}}{space 1}
{space 4}{space 0}{ralign 12:renewable}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6067}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2926}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5462}}}{space 1}
{space 4}{space 0}{ralign 12:methane}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6905}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0378}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5218}}}{space 1}
{space 4}{space 0}{ralign 12:seawalls}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4442}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0019}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8027}}}{space 1}
{space 4}{space 0}{ralign 12:vehicle}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7559}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0877}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4209}}}{space 1}
{space 4}{space 0}{ralign 12:gas}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5615}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2712}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6112}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. 
. ** economy
. polychoric q7 q9
{res}
{txt}Variables :  {res}q7 q9
{txt}Type :       {res}polychoric
{txt}Rho        = {res}.43917163
{txt}S.e.       = {res}.03623147
{txt}Goodness of fit tests:
Pearson G2 = {res}86.275575{txt}, Prob( >chi2({res}8{txt})) = {res}2.645e-15
{txt}LR X2      = {res}129.48289{txt}, Prob( >chi2({res}8{txt})) = {res}3.621e-24
{txt}
{com}. display r(sum_w)
{res}1021
{txt}
{com}. global N=r(sum_w)
{txt}
{com}. matrix r=r(R)
{txt}
{com}. factormat r, n($N) factors(2)
{txt}(obs=1021)

Factor analysis/correlation{col 52}Number of obs    = {res}    1021
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       1
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       1

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.63204      0.87834            1.6385       1.6385
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}     -0.24630            .           -0.6385       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}1{txt})  ={res}  218.45{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q7}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5622}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6840}}}{space 1}
{space 4}{space 0}{ralign 12:q9}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5622}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6840}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. 
. ** nep
. polychoric q20 q22 q24 q29 q31 q33
{res}
{txt}Polychoric correlation matrix

           q20        q22        q24        q29        q31        q33
q20  {res}        1
{txt}q22  {res}.35273251          1
{txt}q24  {res}.41100198  .56053546          1
{txt}q29  {res}.48337277  .30003974  .36322039          1
{txt}q31  {res}.36026443  .52479819  .51631277  .43078282          1
{txt}q33  {res}.43420349  .50565599  .63654066  .39682955  .53613206          1
{txt}
{com}. display r(sum_w)
{res}970
{txt}
{com}. global N=r(sum_w)
{txt}
{com}. matrix r=r(R)
{txt}
{com}. factormat r, n($N) factors(2)
{txt}(obs=970)

Factor analysis/correlation{col 52}Number of obs    = {res}     970
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}      11

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.71438      2.50286            1.0953       1.0953
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.21153      0.22011            0.0854       1.1806
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.00858      0.06956           -0.0035       1.1772
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.07814      0.07335           -0.0315       1.1457
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}     -0.15149      0.05798           -0.0611       1.0845
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}     -0.20947            .           -0.0845       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}15{txt}) ={res} 2032.59{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q20}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5829}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2543}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5955}}}{space 1}
{space 4}{space 0}{ralign 12:q22}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6674}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1744}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5242}}}{space 1}
{space 4}{space 0}{ralign 12:q24}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7495}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1494}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4159}}}{space 1}
{space 4}{space 0}{ralign 12:q29}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5646}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2943}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5947}}}{space 1}
{space 4}{space 0}{ralign 12:q31}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6962}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0413}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5136}}}{space 1}
{space 4}{space 0}{ralign 12:q33}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7509}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0762}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4303}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. 
. ** domain specific knowledge
. polychoric q62 q63 q66 q67
{res}
{txt}Polychoric correlation matrix

           q62        q63        q66        q67
q62  {res}        1
{txt}q63  {res}.33263921          1
{txt}q66  {res}.18998333  .13025676          1
{txt}q67  {res}.11927385  -.0313108  .15133714          1
{txt}
{com}. display r(sum_w)
{res}1085
{txt}
{com}. global N=r(sum_w)
{txt}
{com}. matrix r=r(R)
{txt}
{com}. factormat r, n($N) factors(2)
{txt}(obs=1085)

Factor analysis/correlation{col 52}Number of obs    = {res}    1085
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      0.59639      0.46859            1.6376       1.6376
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.12781      0.24662            0.3509       1.9885
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.11882      0.12238           -0.3263       1.6623
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.24120            .           -0.6623       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res}  215.13{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q62}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5101}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0365}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7385}}}{space 1}
{space 4}{space 0}{ralign 12:q63}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4384}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1797}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.7756}}}{space 1}
{space 4}{space 0}{ralign 12:q66}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3364}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1495}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8645}}}{space 1}
{space 4}{space 0}{ralign 12:q67}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1757}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2680}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.8973}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. 
. ** network interest
. polychoric q81 q82 q83 q85
{res}
{txt}Polychoric correlation matrix

           q81        q82        q83        q85
q81  {res}        1
{txt}q82  {res}.78255969          1
{txt}q83  {res}.49032185   .5575268          1
{txt}q85  {res}.42353143  .50747353  .74889158          1
{txt}
{com}. display r(sum_w)
{res}1080
{txt}
{com}. global N=r(sum_w)
{txt}
{com}. matrix r=r(R)
{txt}
{com}. factormat r, n($N) factors(2)
{txt}(obs=1080)

Factor analysis/correlation{col 52}Number of obs    = {res}    1080
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.37269      1.97898            0.9665       0.9665
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.39371      0.53966            0.1604       1.1268
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.14594      0.01947           -0.0594       1.0674
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.16541            .           -0.0674       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res} 2351.46{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q81}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7562}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3395}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3129}}}{space 1}
{space 4}{space 0}{ralign 12:q82}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8143}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2731}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2624}}}{space 1}
{space 4}{space 0}{ralign 12:q83}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7770}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2954}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3090}}}{space 1}
{space 4}{space 0}{ralign 12:q85}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7308}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3415}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3493}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. 
. ** risk 
. polychoric q101 q102 q103
{res}
{txt}Polychoric correlation matrix

           q101       q102       q103
q101  {res}        1
{txt}q102  {res}.69842382          1
{txt}q103  {res}.76951202   .6906541          1
{txt}
{com}. display r(sum_w)
{res}980
{txt}
{com}. global N=r(sum_w)
{txt}
{com}. matrix r=r(R)
{txt}
{com}. factormat r, n($N) factors(2)
{txt}(obs=980)

Factor analysis/correlation{col 52}Number of obs    = {res}     980
{col 5}{txt}Method: principal factors{col 52}Retained factors = {res}       1
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       3

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.05171      2.14663            1.1219       1.1219
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}     -0.09492      0.03310           -0.0519       1.0700
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.12802            .           -0.0700       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}3{txt})  ={res} 1648.29{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:q101}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8513}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2754}}}{space 1}
{space 4}{space 0}{ralign 12:q102}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7824}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3879}}}{space 1}
{space 4}{space 0}{ralign 12:q103}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8456}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2850}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}. 
. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. 
. ************************
. ** BINARY GLLAAM MODELS
. *************************
. **RECODE INTO BINARY ITEMS AND ESTIMATE IRT 
. ** 1 support 0 oppose 
. recode emission 1 = 0 2 = 0 3 = 1 4 = 1
{txt}(emission: 1036 changes made)

{com}. recode tax_industry 1 = 0 2 = 0 3 = 1 4 = 1
{txt}(tax_industry: 1049 changes made)

{com}. recode tax_individuals 1 = 0 2 = 0 3 = 1 4 = 1
{txt}(tax_individuals: 1038 changes made)

{com}. recode educatepublic 1 = 0 2 = 0 3 = 1 4 = 1
{txt}(educatepublic: 1074 changes made)

{com}. recode kyoto 1 = 0 2 = 0 3 = 1 4 = 1
{txt}(kyoto: 978 changes made)

{com}. recode law 1 = 0 2 = 0 3 = 1 4 = 1
{txt}(law: 1058 changes made)

{com}. recode renewable 1 = 0 2 = 0 3 = 1 4 = 1
{txt}(renewable: 1061 changes made)

{com}. recode methane 1 = 0 2 = 0 3 = 1 4 = 1
{txt}(methane: 972 changes made)

{com}. recode seawalls 1 = 0 2 = 0 3 = 1 4 = 1
{txt}(seawalls: 971 changes made)

{com}. recode vehicle 1 = 0 2 = 0 3 = 1 4 = 1
{txt}(vehicle: 1073 changes made)

{com}. recode gas 1 = 0 2 = 0 3 = 1 4 = 1
{txt}(gas: 1058 changes made)

{com}. recode setprice 1 = 0 2 = 0 3 = 1 4 = 1
{txt}(setprice: 1063 changes made)

{com}. 
. 
. ** rename response/outcome variables
. rename emission item_1
{txt}
{com}. rename tax_industry item_2
{txt}
{com}. rename tax_individuals item_3
{txt}
{com}. rename educatepublic item_4
{txt}
{com}. rename kyoto item_5
{txt}
{com}. rename law item_6 
{txt}
{com}. rename renewable item_7
{txt}
{com}. rename methane item_8
{txt}
{com}. rename seawalls item_9
{txt}
{com}. rename vehicle item_10
{txt}
{com}. rename gas item_11
{txt}
{com}. rename setprice item_12
{txt}
{com}. 
. ** change data into long form 
. reshape long item_, i(id) j(item)
{txt}(note: j = 1 2 3 4 5 6 7 8 9 10 11 12)

Data{col 36}wide{col 43}->{col 48}long
{hline 77}
Number of obs.                 {res}    1093   {txt}->{res}   13116
{txt}Number of variables            {res}     264   {txt}->{res}     254
{txt}j variable (12 values)                    ->   {res}item
{txt}xij variables:
              {res}item_1 item_2 ... item_12   {txt}->   {res}item_
{txt}{hline 77}

{com}. rename item_ y
{txt}
{com}. qui tab item, gen(d)
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. 
. *******************
. **ESTIMATION 
. ********************
. ** model specification 
. eq load: d1-d12
{txt}
{com}. eq thr: d2-d12
{txt}
{com}. eq f1: race gender education ideo pid risk nep economy network  
{txt}
{com}. 
. ** estimate structural model (no het) 
. gllamm y, i(id) l(oprob) f(binom) thres(thr) eqs(load) geqs(f1) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-4479.5146
{txt}Iteration 1:    log likelihood = {res}-4303.6451
{txt}Iteration 2:    log likelihood = {res}-4101.7917
{txt}Iteration 3:    log likelihood = {res}-4082.5558
{txt}Iteration 4:    log likelihood = {res}-4080.1719
{txt}Iteration 5:    log likelihood = {res}-4078.5061
{txt}Iteration 6:    log likelihood = {res}-4077.8926
{txt}Iteration 7:    log likelihood = {res}-4077.8407
{txt}Iteration 8:    log likelihood = {res}-4077.8407


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-4077.8407{txt}  
Iteration 1:{col 16}log likelihood = {res}-4077.8407{txt}  (backed up)
Iteration 2:{col 16}log likelihood = {res}-4077.6969{txt}  
Iteration 3:{col 16}log likelihood = {res}-4077.6961{txt}  
Iteration 4:{col 16}log likelihood = {res}-4077.6961{txt}  
{res} 
{txt}number of level 1 units = {res}11378
{txt}number of level 2 units = {res}984
 
{txt}Condition Number = {res}907.1041
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-4077.6961
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}d2 {c |}{col 14}{res}{space 2}  1.16963{col 26}{space 2} .2734861{col 37}{space 1}    4.28{col 46}{space 3}0.000{col 54}{space 4} .6336074{col 67}{space 3} 1.705653
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 2.088342{col 26}{space 2} .2592637{col 37}{space 1}    8.05{col 46}{space 3}0.000{col 54}{space 4} 1.580194{col 67}{space 3} 2.596489
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.2070247{col 26}{space 2}  .217329{col 37}{space 1}   -0.95{col 46}{space 3}0.341{col 54}{space 4}-.6329816{col 67}{space 3} .2189323
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 1.015566{col 26}{space 2} .4335532{col 37}{space 1}    2.34{col 46}{space 3}0.019{col 54}{space 4}  .165817{col 67}{space 3} 1.865314
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .4865589{col 26}{space 2} .2239442{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .0476364{col 67}{space 3} .9254814
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.6532274{col 26}{space 2} .1516686{col 37}{space 1}   -4.31{col 46}{space 3}0.000{col 54}{space 4}-.9504924{col 67}{space 3}-.3559624
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .7206908{col 26}{space 2} .1900174{col 37}{space 1}    3.79{col 46}{space 3}0.000{col 54}{space 4} .3482635{col 67}{space 3} 1.093118
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .6079924{col 26}{space 2} .1081052{col 37}{space 1}    5.62{col 46}{space 3}0.000{col 54}{space 4} .3961102{col 67}{space 3} .8198746
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .0740111{col 26}{space 2} .2807528{col 37}{space 1}    0.26{col 46}{space 3}0.792{col 54}{space 4}-.4762544{col 67}{space 3} .6242765
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.805919{col 26}{space 2} .1651131{col 37}{space 1}   10.94{col 46}{space 3}0.000{col 54}{space 4} 1.482303{col 67}{space 3} 2.129535
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .6934775{col 26}{space 2} .1738168{col 37}{space 1}    3.99{col 46}{space 3}0.000{col 54}{space 4} .3528027{col 67}{space 3} 1.034152
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.7913848{col 26}{space 2} .1243987{col 37}{space 1}   -6.36{col 46}{space 3}0.000{col 54}{space 4}-1.035202{col 67}{space 3}-.5475678
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.08018654 (.02738339)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}3.5166286 (.66176162)
{txt}    d3: {res}3.2203248 (.60728769)
{txt}    d4: {res}2.8922773 (.58692478)
{txt}    d5: {res}5.1719756 (1.0605114)
{txt}    d6: {res}2.9913867 (.57985661)
{txt}    d7: {res}1.9406208 (.44269688)
{txt}    d8: {res}2.5601881 (.48928045)
{txt}    d9: {res}1.2790792 (.26518926)
{txt}    d10: {res}3.6193055 (.72239678)
{txt}    d11: {res}2.110716 (.40048107)
{txt}    d12: {res}2.3826048 (.45311512)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}.00334225 (.0329119)
{txt}    gender: {res}-.04428712 (.02497155)
{txt}    education: {res}.00726379 (.01147684)
{txt}    ideo: {res}-.02053554 (.00892915)
{txt}    pid: {res}.05304008 (.02047847)
{txt}    risk: {res}.49810693 (.10530615)
{txt}    nep: {res}.47486045 (.1126707)
{txt}    economy: {res}-.31297274 (.07616217)
{txt}    network: {res}.0382521 (.03934837)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix a=e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,33]
        _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:
            d2          d3          d4          d5          d6          d7          d8          d9         d10
y1 {res}  1.1696303   2.0883419  -.20702466   1.0155656   .48655889  -.65322742   .72069081   .60799238   .07401106

{txt}        _cut11:     _cut11:     _cut11:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:
           d11         d12       _cons          d2          d3          d4          d5          d6          d7
y1 {res}  1.8059188   .69347749   -.7913848   3.5166286   3.2203248   2.8922773   5.1719756   2.9913867   1.9406208

{txt}        id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:      id1_1:         f1:         f1:         f1:
            d8          d9         d10         d11         d12          d1        race      gender   education
y1 {res}  2.5601881   1.2790792   3.6193055    2.110716   2.3826048   .28317227   .00334225  -.04428712   .00726379

{txt}            f1:         f1:         f1:         f1:         f1:         f1:
          ideo         pid        risk         nep     economy     network
y1 {res} -.02053554   .05304008   .49810693   .47486045  -.31297274    .0382521
{reset}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. ** MODEL 1 (Baseline het model)
. ** specification of het  
. eq het: education gw_know pluralism 
{txt}
{com}. 
. ** estimate structural and variance equation 
. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(a) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-4072.2163
{txt}Iteration 1:    log likelihood = {res}  -4067.65
{txt}Iteration 2:    log likelihood = {res} -4065.084
{txt}Iteration 3:    log likelihood = {res}-4064.8556
{txt}Iteration 4:    log likelihood = {res}-4064.8556


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-4064.8556{txt}  
Iteration 1:{col 16}log likelihood = {res}-4064.8556{txt}  (backed up)
Iteration 2:{col 16}log likelihood = {res}-4064.4704{txt}  
Iteration 3:{col 16}log likelihood = {res}-4063.8252{txt}  
Iteration 4:{col 16}log likelihood = {res}-4063.7657{txt}  
Iteration 5:{col 16}log likelihood = {res}-4063.7624{txt}  
Iteration 6:{col 16}log likelihood = {res}-4063.7624{txt}  
{res} 
{txt}number of level 1 units = {res}11355
{txt}number of level 2 units = {res}982
 
{txt}Condition Number = {res}807.73359
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-4063.7624
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}d2 {c |}{col 14}{res}{space 2} .8930535{col 26}{space 2} .2507011{col 37}{space 1}    3.56{col 46}{space 3}0.000{col 54}{space 4} .4016884{col 67}{space 3} 1.384419
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.667435{col 26}{space 2} .2706394{col 37}{space 1}    6.16{col 46}{space 3}0.000{col 54}{space 4} 1.136991{col 67}{space 3} 2.197878
{txt}{space 10}d4 {c |}{col 14}{res}{space 2} -.240013{col 26}{space 2} .1853957{col 37}{space 1}   -1.29{col 46}{space 3}0.195{col 54}{space 4}-.6033819{col 67}{space 3} .1233559
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .7038714{col 26}{space 2} .3829227{col 37}{space 1}    1.84{col 46}{space 3}0.066{col 54}{space 4}-.0466433{col 67}{space 3} 1.454386
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .3210478{col 26}{space 2} .1898061{col 37}{space 1}    1.69{col 46}{space 3}0.091{col 54}{space 4}-.0509653{col 67}{space 3} .6930608
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.6092825{col 26}{space 2} .1348346{col 37}{space 1}   -4.52{col 46}{space 3}0.000{col 54}{space 4}-.8735535{col 67}{space 3}-.3450116
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .5298167{col 26}{space 2}  .166017{col 37}{space 1}    3.19{col 46}{space 3}0.001{col 54}{space 4} .2044293{col 67}{space 3} .8552041
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .4907272{col 26}{space 2} .0965072{col 37}{space 1}    5.08{col 46}{space 3}0.000{col 54}{space 4} .3015765{col 67}{space 3} .6798779
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} -.042031{col 26}{space 2} .2339249{col 37}{space 1}   -0.18{col 46}{space 3}0.857{col 54}{space 4}-.5005153{col 67}{space 3} .4164534
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.469698{col 26}{space 2} .1958506{col 37}{space 1}    7.50{col 46}{space 3}0.000{col 54}{space 4} 1.085838{col 67}{space 3} 1.853558
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .5286935{col 26}{space 2} .1550494{col 37}{space 1}    3.41{col 46}{space 3}0.001{col 54}{space 4} .2248022{col 67}{space 3} .8325847
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.7050972{col 26}{space 2} .1202725{col 37}{space 1}   -5.86{col 46}{space 3}0.000{col 54}{space 4} -.940827{col 67}{space 3}-.4693673
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}-.02967347 (.02237855)
{txt}    gw_know: {res}.09225171 (.08229876)
{txt}    pluralism: {res}-.34476735 (.08872883)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.07084654 (.02599031)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}3.1570036 (.55370063)
{txt}    d3: {res}2.8325409 (.50337753)
{txt}    d4: {res}2.7181973 (.51335395)
{txt}    d5: {res}4.7048334 (.90551774)
{txt}    d6: {res}2.712428 (.48944597)
{txt}    d7: {res}1.8585623 (.39217644)
{txt}    d8: {res}2.2902496 (.40834975)
{txt}    d9: {res}1.1981771 (.2310601)
{txt}    d10: {res}3.2793584 (.61230542)
{txt}    d11: {res}1.8474546 (.33245343)
{txt}    d12: {res}2.1817925 (.38516301)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}.01123145 (.03133947)
{txt}    gender: {res}-.03780555 (.0236184)
{txt}    education: {res}.0016904 (.01206082)
{txt}    ideo: {res}-.01967592 (.00860858)
{txt}    pid: {res}.05055708 (.01979503)
{txt}    risk: {res}.46715517 (.10459998)
{txt}    nep: {res}.4693564 (.11441234)
{txt}    economy: {res}-.38608165 (.09011804)
{txt}    network: {res}.04750969 (.03787457)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix b = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,36]
        _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:     _cut11:
            d2          d3          d4          d5          d6          d7          d8          d9         d10
y1 {res}  .89305347   1.6674345    -.240013   .70387143   .32104776  -.60928254   .52981667    .4907272  -.04203097

{txt}        _cut11:     _cut11:     _cut11:       lns1:       lns1:       lns1:     id1_1l:     id1_1l:     id1_1l:
           d11         d12       _cons   education     gw_know   pluralism          d2          d3          d4
y1 {res}  1.4696983   .52869346  -.70509716  -.02967347   .09225171  -.34476735   3.1570036   2.8325409   2.7181973

{txt}        id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:     id1_1l:      id1_1:
            d5          d6          d7          d8          d9         d10         d11         d12          d1
y1 {res}  4.7048334    2.712428   1.8585623   2.2902496   1.1981771   3.2793584   1.8474546   2.1817925   .26617014

{txt}            f1:         f1:         f1:         f1:         f1:         f1:         f1:         f1:         f1:
          race      gender   education        ideo         pid        risk         nep     economy     network
y1 {res}  .01123145  -.03780555    .0016904  -.01967592   .05055708   .46715517    .4693564  -.38608165   .04750969
{reset}
{com}. 
. ** MODEL 2 (domain-specific * pluralism) 
. ** specification of het  
. eq het: education gw_know pluralism gwknow_plural
{txt}
{com}. 
. ** estimate structural and variance equation 
. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(b) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-4063.7621
{txt}Iteration 1:    log likelihood = {res}-4063.7621


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res} -4063.762{txt}  
Iteration 1:{col 16}log likelihood = {res} -4063.762{txt}  (backed up)
Iteration 2:{col 16}log likelihood = {res}-4063.7544{txt}  
Iteration 3:{col 16}log likelihood = {res}-4063.7544{txt}  
{res} 
{txt}number of level 1 units = {res}11355
{txt}number of level 2 units = {res}982
 
{txt}Condition Number = {res}810.90479
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-4063.7544
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}d2 {c |}{col 14}{res}{space 2} .8894238{col 26}{space 2} .2514558{col 37}{space 1}    3.54{col 46}{space 3}0.000{col 54}{space 4} .3965794{col 67}{space 3} 1.382268
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.660083{col 26}{space 2} .2759388{col 37}{space 1}    6.02{col 46}{space 3}0.000{col 54}{space 4} 1.119253{col 67}{space 3} 2.200913
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.2391977{col 26}{space 2} .1848095{col 37}{space 1}   -1.29{col 46}{space 3}0.196{col 54}{space 4}-.6014177{col 67}{space 3} .1230223
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .7009692{col 26}{space 2} .3822634{col 37}{space 1}    1.83{col 46}{space 3}0.067{col 54}{space 4}-.0482533{col 67}{space 3} 1.450192
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .3194912{col 26}{space 2} .1894563{col 37}{space 1}    1.69{col 46}{space 3}0.092{col 54}{space 4}-.0518364{col 67}{space 3} .6908188
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.6071564{col 26}{space 2} .1354077{col 37}{space 1}   -4.48{col 46}{space 3}0.000{col 54}{space 4}-.8725505{col 67}{space 3}-.3417622
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .5273059{col 26}{space 2} .1665723{col 37}{space 1}    3.17{col 46}{space 3}0.002{col 54}{space 4} .2008301{col 67}{space 3} .8537816
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .4883816{col 26}{space 2} .0979512{col 37}{space 1}    4.99{col 46}{space 3}0.000{col 54}{space 4} .2964007{col 67}{space 3} .6803624
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.0421023{col 26}{space 2} .2329584{col 37}{space 1}   -0.18{col 46}{space 3}0.857{col 54}{space 4}-.4986924{col 67}{space 3} .4144877
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.463294{col 26}{space 2}  .201746{col 37}{space 1}    7.25{col 46}{space 3}0.000{col 54}{space 4} 1.067879{col 67}{space 3} 1.858709
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .5264749{col 26}{space 2} .1554939{col 37}{space 1}    3.39{col 46}{space 3}0.001{col 54}{space 4} .2217124{col 67}{space 3} .8312374
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.7019312{col 26}{space 2} .1224916{col 37}{space 1}   -5.73{col 46}{space 3}0.000{col 54}{space 4}-.9420104{col 67}{space 3} -.461852
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}-.02946777 (.02243875)
{txt}    gw_know: {res}.08218921 (.11573028)
{txt}    pluralism: {res}-.36318531 (.17324837)
{txt}    gwknow_plural: {res}.03881273 (.31375055)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.07028652 (.02617978)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}3.1572712 (.55376493)
{txt}    d3: {res}2.8315105 (.50328518)
{txt}    d4: {res}2.7185441 (.51346133)
{txt}    d5: {res}4.7061787 (.90587203)
{txt}    d6: {res}2.7122859 (.48941918)
{txt}    d7: {res}1.8585182 (.39222809)
{txt}    d8: {res}2.2901176 (.4083305)
{txt}    d9: {res}1.1981289 (.23105148)
{txt}    d10: {res}3.2788797 (.61224815)
{txt}    d11: {res}1.8467433 (.33239506)
{txt}    d12: {res}2.1821515 (.38523189)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}.01111307 (.0312296)
{txt}    gender: {res}-.03759642 (.02358086)
{txt}    education: {res}.00172664 (.01201817)
{txt}    ideo: {res}-.01958812 (.00860245)
{txt}    pid: {res}.05043343 (.01974901)
{txt}    risk: {res}.46503568 (.10554257)
{txt}    nep: {res}.4676204 (.11484378)
{txt}    economy: {res}-.38472948 (.09045914)
{txt}    network: {res}.04716362 (.03782062)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix c = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,37]
          _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    .88942375     1.6600828    -.23919771     .70096918     .31949123    -.60715637     .52730588     .48838156

{txt}          _cut11:       _cut11:       _cut11:       _cut11:         lns1:         lns1:         lns1:         lns1:
             d10           d11           d12         _cons     education       gw_know     pluralism  gwknow_plu~l
y1 {res}   -.04210234     1.4632943     .52647491    -.70193118    -.02946777     .08218921    -.36318531     .03881273

{txt}          id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    3.1572712     2.8315105     2.7185441     4.7061787     2.7122859     1.8585182     2.2901176     1.1981289

{txt}          id1_1l:       id1_1l:       id1_1l:        id1_1:           f1:           f1:           f1:           f1:
             d10           d11           d12            d1          race        gender     education          ideo
y1 {res}    3.2788797     1.8467433     2.1821515     .26511605     .01111307    -.03759642     .00172664    -.01958812

{txt}              f1:           f1:           f1:           f1:           f1:
             pid          risk           nep       economy       network
y1 {res}    .05043343     .46503568      .4676204    -.38472948     .04716362
{reset}
{com}. 
. ** MODEL 3 (education * pluralism) 
. ** specification of het  
. eq het: education gw_know pluralism educ_plural
{txt}
{com}. 
. ** estimate structural and variance equation 
. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(b) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-4063.7621
{txt}Iteration 1:    log likelihood = {res}-4063.4087
{txt}Iteration 2:    log likelihood = {res}-4062.2137
{txt}Iteration 3:    log likelihood = {res}-4062.2137


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-4062.2137{txt}  
Iteration 1:{col 16}log likelihood = {res}-4062.2137{txt}  (not concave)
Iteration 2:{col 16}log likelihood = {res}-4061.7679{txt}  
Iteration 3:{col 16}log likelihood = {res}-4061.7028{txt}  (not concave)
Iteration 4:{col 16}log likelihood = {res}-4061.7002{txt}  
Iteration 5:{col 16}log likelihood = {res}-4061.6995{txt}  
Iteration 6:{col 16}log likelihood = {res}-4061.6995{txt}  
{res} 
{txt}number of level 1 units = {res}11355
{txt}number of level 2 units = {res}982
 
{txt}Condition Number = {res}710.73038
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-4061.6995
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.050265{col 26}{space 2} .2974874{col 37}{space 1}    3.53{col 46}{space 3}0.000{col 54}{space 4} .4672007{col 67}{space 3}  1.63333
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.923877{col 26}{space 2} .3359919{col 37}{space 1}    5.73{col 46}{space 3}0.000{col 54}{space 4} 1.265345{col 67}{space 3} 2.582409
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.2489408{col 26}{space 2} .2122044{col 37}{space 1}   -1.17{col 46}{space 3}0.241{col 54}{space 4}-.6648539{col 67}{space 3} .1669723
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .8676412{col 26}{space 2}   .44831{col 37}{space 1}    1.94{col 46}{space 3}0.053{col 54}{space 4}-.0110302{col 67}{space 3} 1.746313
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .3968045{col 26}{space 2} .2238706{col 37}{space 1}    1.77{col 46}{space 3}0.076{col 54}{space 4}-.0419738{col 67}{space 3} .8355827
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.6818603{col 26}{space 2}  .158898{col 37}{space 1}   -4.29{col 46}{space 3}0.000{col 54}{space 4}-.9932947{col 67}{space 3} -.370426
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .6307911{col 26}{space 2} .1998931{col 37}{space 1}    3.16{col 46}{space 3}0.002{col 54}{space 4} .2390078{col 67}{space 3} 1.022574
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5696428{col 26}{space 2} .1202349{col 37}{space 1}    4.74{col 46}{space 3}0.000{col 54}{space 4} .3339866{col 67}{space 3}  .805299
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.0059086{col 26}{space 2} .2704192{col 37}{space 1}   -0.02{col 46}{space 3}0.983{col 54}{space 4}-.5359204{col 67}{space 3} .5241032
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.701928{col 26}{space 2} .2573158{col 37}{space 1}    6.61{col 46}{space 3}0.000{col 54}{space 4} 1.197599{col 67}{space 3} 2.206258
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .6257343{col 26}{space 2}  .185989{col 37}{space 1}    3.36{col 46}{space 3}0.001{col 54}{space 4} .2612027{col 67}{space 3}  .990266
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.7924669{col 26}{space 2} .1449208{col 37}{space 1}   -5.47{col 46}{space 3}0.000{col 54}{space 4}-1.076506{col 67}{space 3}-.5084272
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}.00782794 (.02894233)
{txt}    gw_know: {res}.08136654 (.08235417)
{txt}    pluralism: {res}.14685422 (.25919504)
{txt}    educ_plural: {res}-.14070356 (.0694268)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.09219076 (.03585545)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}3.1300534 (.54843046)
{txt}    d3: {res}2.801463 (.4973354)
{txt}    d4: {res}2.7143509 (.51277943)
{txt}    d5: {res}4.6980887 (.90327265)
{txt}    d6: {res}2.7293741 (.49169714)
{txt}    d7: {res}1.8510738 (.39097553)
{txt}    d8: {res}2.2973296 (.40936597)
{txt}    d9: {res}1.2108468 (.23280395)
{txt}    d10: {res}3.2935757 (.61449216)
{txt}    d11: {res}1.8554045 (.33342723)
{txt}    d12: {res}2.1778454 (.38439865)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}.01203513 (.03584154)
{txt}    gender: {res}-.04457679 (.02728353)
{txt}    education: {res}.00529763 (.01409881)
{txt}    ideo: {res}-.02255327 (.00995884)
{txt}    pid: {res}.05941236 (.02322799)
{txt}    risk: {res}.53986478 (.12627134)
{txt}    nep: {res}.54168325 (.13661207)
{txt}    economy: {res}-.44003887 (.10677046)
{txt}    network: {res}.05075366 (.04314029)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix d = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,37]
         _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:
             d2           d3           d4           d5           d6           d7           d8           d9
y1 {res}   1.0502652    1.9238774   -.24894082    .86764118    .39680448   -.68186031    .63079113    .56964278

{txt}         _cut11:      _cut11:      _cut11:      _cut11:        lns1:        lns1:        lns1:        lns1:
            d10          d11          d12        _cons    education      gw_know    pluralism  educ_plural
y1 {res}  -.00590857    1.7019284    .62573433   -.79246686    .00782794    .08136654    .14685422   -.14070356

{txt}         id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:
             d2           d3           d4           d5           d6           d7           d8           d9
y1 {res}   3.1300534     2.801463    2.7143509    4.6980887    2.7293741    1.8510738    2.2973296    1.2108468

{txt}         id1_1l:      id1_1l:      id1_1l:       id1_1:          f1:          f1:          f1:          f1:
            d10          d11          d12           d1         race       gender    education         ideo
y1 {res}   3.2935757    1.8554045    2.1778454    .30362932    .01203513   -.04457679    .00529763   -.02255327

{txt}             f1:          f1:          f1:          f1:          f1:
            pid         risk          nep      economy      network
y1 {res}   .05941236    .53986478    .54168325   -.44003887    .05075366
{reset}
{com}. 
. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. ** MODEL 4 (value trade-off) 
. ** specification of het  
. eq het: education gw_know pluralism educ_plural economy nep
{txt}
{com}. 
. ** estimate structural and variance equation 
. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(d) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-4061.6981
{txt}Iteration 1:    log likelihood = {res}-4059.1985
{txt}Iteration 2:    log likelihood = {res}-4051.3022
{txt}Iteration 3:    log likelihood = {res}-4049.5525
{txt}Iteration 4:    log likelihood = {res}-4048.9046
{txt}Iteration 5:    log likelihood = {res} -4048.823
{txt}Iteration 6:    log likelihood = {res}-4048.2303
{txt}Iteration 7:    log likelihood = {res}-4048.2303


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-4048.2303{txt}  
Iteration 1:{col 16}log likelihood = {res}-4048.2303{txt}  (backed up)
Iteration 2:{col 16}log likelihood = {res}-4047.7174{txt}  
Iteration 3:{col 16}log likelihood = {res}-4047.4983{txt}  (not concave)
Iteration 4:{col 16}log likelihood = {res}-4047.0349{txt}  
Iteration 5:{col 16}log likelihood = {res} -4047.018{txt}  
Iteration 6:{col 16}log likelihood = {res}-4047.0177{txt}  
{res} 
{txt}number of level 1 units = {res}11355
{txt}number of level 2 units = {res}982
 
{txt}Condition Number = {res}440.97552
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-4047.0177
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.912817{col 26}{space 2} .5616587{col 37}{space 1}    3.41{col 46}{space 3}0.001{col 54}{space 4} .8119862{col 67}{space 3} 3.013648
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 3.030735{col 26}{space 2} .6468104{col 37}{space 1}    4.69{col 46}{space 3}0.000{col 54}{space 4}  1.76301{col 67}{space 3}  4.29846
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.0186913{col 26}{space 2} .3420409{col 37}{space 1}   -0.05{col 46}{space 3}0.956{col 54}{space 4}-.6890792{col 67}{space 3} .6516965
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} 2.020418{col 26}{space 2} .8348133{col 37}{space 1}    2.42{col 46}{space 3}0.016{col 54}{space 4} .3842137{col 67}{space 3} 3.656622
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .9480091{col 26}{space 2} .4184679{col 37}{space 1}    2.27{col 46}{space 3}0.023{col 54}{space 4} .1278271{col 67}{space 3} 1.768191
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.8334842{col 26}{space 2} .2508343{col 37}{space 1}   -3.32{col 46}{space 3}0.001{col 54}{space 4} -1.32511{col 67}{space 3}-.3418579
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} 1.192671{col 26}{space 2} .3882043{col 37}{space 1}    3.07{col 46}{space 3}0.002{col 54}{space 4} .4318046{col 67}{space 3} 1.953538
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .9116801{col 26}{space 2} .2338678{col 37}{space 1}    3.90{col 46}{space 3}0.000{col 54}{space 4} .4533076{col 67}{space 3} 1.370053
{txt}{space 9}d10 {c |}{col 14}{res}{space 2} .4546732{col 26}{space 2} .4636606{col 37}{space 1}    0.98{col 46}{space 3}0.327{col 54}{space 4} -.454085{col 67}{space 3} 1.363431
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 2.585295{col 26}{space 2} .5093079{col 37}{space 1}    5.08{col 46}{space 3}0.000{col 54}{space 4} 1.587069{col 67}{space 3}  3.58352
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} 1.173859{col 26}{space 2} .3648234{col 37}{space 1}    3.22{col 46}{space 3}0.001{col 54}{space 4} .4588185{col 67}{space 3}   1.8889
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.9496004{col 26}{space 2} .2379493{col 37}{space 1}   -3.99{col 46}{space 3}0.000{col 54}{space 4}-1.415973{col 67}{space 3}-.4832283
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}.02145882 (.02903965)
{txt}    gw_know: {res}.02485251 (.08258433)
{txt}    pluralism: {res}.27574561 (.27526318)
{txt}    educ_plural: {res}-.13507033 (.07003092)
{txt}    economy: {res}-.21273794 (.15267651)
{txt}    nep: {res}.79341371 (.16543811)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.27083299 (.11733204)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}2.6485165 (.39789068)
{txt}    d3: {res}2.2286892 (.3487843)
{txt}    d4: {res}2.477036 (.39442897)
{txt}    d5: {res}4.0494883 (.67241782)
{txt}    d6: {res}2.4539023 (.37255261)
{txt}    d7: {res}1.8129981 (.31342381)
{txt}    d8: {res}2.044574 (.30886789)
{txt}    d9: {res}1.1370605 (.18302361)
{txt}    d10: {res}2.9620513 (.47118802)
{txt}    d11: {res}1.4985157 (.24169709)
{txt}    d12: {res}1.9507844 (.29073911)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}.03835672 (.06374627)
{txt}    gender: {res}-.07556709 (.04785736)
{txt}    education: {res}.02057056 (.02633185)
{txt}    ideo: {res}-.03826623 (.01774709)
{txt}    pid: {res}.10361936 (.04192075)
{txt}    risk: {res}.94597018 (.24049624)
{txt}    nep: {res}1.3014986 (.37244708)
{txt}    economy: {res}-.79059541 (.19794007)
{txt}    network: {res}.11054029 (.07783525)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix e = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,39]
         _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:
             d2           d3           d4           d5           d6           d7           d8           d9
y1 {res}   1.9128171    3.0307352   -.01869133    2.0204176    .94800905   -.83348418    1.1926711    .91168009

{txt}         _cut11:      _cut11:      _cut11:      _cut11:        lns1:        lns1:        lns1:        lns1:
            d10          d11          d12        _cons    education      gw_know    pluralism  educ_plural
y1 {res}   .45467321    2.5852946    1.1738593   -.94960043    .02145882    .02485251    .27574561   -.13507033

{txt}           lns1:        lns1:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:
        economy          nep           d2           d3           d4           d5           d6           d7
y1 {res}  -.21273794    .79341371    2.6485165    2.2286892     2.477036    4.0494883    2.4539023    1.8129981

{txt}         id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:       id1_1:          f1:          f1:
             d8           d9          d10          d11          d12           d1         race       gender
y1 {res}    2.044574    1.1370605    2.9620513    1.4985157    1.9507844    .52041617    .03835672   -.07556709

{txt}             f1:          f1:          f1:          f1:          f1:          f1:          f1:
      education         ideo          pid         risk          nep      economy      network
y1 {res}   .02057056   -.03826623    .10361936    .94597018    1.3014986   -.79059541    .11054029
{reset}
{com}. 
. ** MODEL 5 (ideological strength) 
. ** specification of het  
. eq het: education gw_know pluralism educ_plural strength_ideo 
{txt}
{com}. 
. ** estimate structural and variance equation 
. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(d) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-4061.6981
{txt}Iteration 1:    log likelihood = {res}-4061.6934
{txt}Iteration 2:    log likelihood = {res}-4061.5058
{txt}Iteration 3:    log likelihood = {res}-4061.4913
{txt}Iteration 4:    log likelihood = {res}-4061.4913


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-4061.4913{txt}  
Iteration 1:{col 16}log likelihood = {res}-4061.4913{txt}  (backed up)
Iteration 2:{col 16}log likelihood = {res}-4061.4575{txt}  
Iteration 3:{col 16}log likelihood = {res}-4061.3036{txt}  
Iteration 4:{col 16}log likelihood = {res}-4061.2984{txt}  
Iteration 5:{col 16}log likelihood = {res}-4061.2983{txt}  
{res} 
{txt}number of level 1 units = {res}11355
{txt}number of level 2 units = {res}982
 
{txt}Condition Number = {res}715.97978
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-4061.2983
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.069998{col 26}{space 2} .3046147{col 37}{space 1}    3.51{col 46}{space 3}0.000{col 54}{space 4} .4729645{col 67}{space 3} 1.667032
{txt}{space 10}d3 {c |}{col 14}{res}{space 2} 1.968701{col 26}{space 2} .3481857{col 37}{space 1}    5.65{col 46}{space 3}0.000{col 54}{space 4}  1.28627{col 67}{space 3} 2.651133
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.2568476{col 26}{space 2}  .216672{col 37}{space 1}   -1.19{col 46}{space 3}0.236{col 54}{space 4} -.681517{col 67}{space 3} .1678217
{txt}{space 10}d5 {c |}{col 14}{res}{space 2}  .883408{col 26}{space 2} .4590425{col 37}{space 1}    1.92{col 46}{space 3}0.054{col 54}{space 4}-.0162988{col 67}{space 3} 1.783115
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .4013156{col 26}{space 2} .2275513{col 37}{space 1}    1.76{col 46}{space 3}0.078{col 54}{space 4}-.0446769{col 67}{space 3}  .847308
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.7024051{col 26}{space 2}   .16408{col 37}{space 1}   -4.28{col 46}{space 3}0.000{col 54}{space 4}-1.023996{col 67}{space 3}-.3808141
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .6399894{col 26}{space 2} .2034524{col 37}{space 1}    3.15{col 46}{space 3}0.002{col 54}{space 4}   .24123{col 67}{space 3} 1.038749
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5827747{col 26}{space 2} .1239134{col 37}{space 1}    4.70{col 46}{space 3}0.000{col 54}{space 4} .3399089{col 67}{space 3} .8256406
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}-.0114024{col 26}{space 2} .2751529{col 37}{space 1}   -0.04{col 46}{space 3}0.967{col 54}{space 4}-.5506922{col 67}{space 3} .5278873
{txt}{space 9}d11 {c |}{col 14}{res}{space 2}  1.73874{col 26}{space 2} .2662177{col 37}{space 1}    6.53{col 46}{space 3}0.000{col 54}{space 4} 1.216963{col 67}{space 3} 2.260517
{txt}{space 9}d12 {c |}{col 14}{res}{space 2}  .636846{col 26}{space 2} .1901614{col 37}{space 1}    3.35{col 46}{space 3}0.001{col 54}{space 4} .2641365{col 67}{space 3} 1.009556
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.8103403{col 26}{space 2} .1488508{col 37}{space 1}   -5.44{col 46}{space 3}0.000{col 54}{space 4}-1.102083{col 67}{space 3}-.5185981
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}.00482489 (.02908467)
{txt}    gw_know: {res}.0833775 (.08236365)
{txt}    pluralism: {res}.13671676 (.25935189)
{txt}    educ_plural: {res}-.1358027 (.06965972)
{txt}    strength_ideo: {res}.01856201 (.0207519)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.09470167 (.03719942)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}3.1536312 (.55819876)
{txt}    d3: {res}2.8400737 (.51002533)
{txt}    d4: {res}2.7247193 (.51994704)
{txt}    d5: {res}4.7403913 (.92113877)
{txt}    d6: {res}2.73437 (.4975107)
{txt}    d7: {res}1.8484163 (.39514242)
{txt}    d8: {res}2.3023262 (.41449714)
{txt}    d9: {res}1.2179759 (.23647005)
{txt}    d10: {res}3.3025811 (.62197253)
{txt}    d11: {res}1.8747595 (.34026994)
{txt}    d12: {res}2.1880662 (.39027102)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}.01075388 (.0362992)
{txt}    gender: {res}-.04578925 (.02772499)
{txt}    education: {res}.00489744 (.01423703)
{txt}    ideo: {res}-.02300878 (.01014663)
{txt}    pid: {res}.05974959 (.0235206)
{txt}    risk: {res}.54682433 (.12876552)
{txt}    nep: {res}.5493917 (.13931463)
{txt}    economy: {res}-.44322866 (.10828259)
{txt}    network: {res}.0525385 (.04380967)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix f = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,38]
          _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:       _cut11:
              d2            d3            d4            d5            d6            d7            d8            d9
y1 {res}    1.0699983     1.9687013    -.25684764     .88340797     .40131556    -.70240506     .63998938     .58277472

{txt}          _cut11:       _cut11:       _cut11:       _cut11:         lns1:         lns1:         lns1:         lns1:
             d10           d11           d12         _cons     education       gw_know     pluralism   educ_plural
y1 {res}   -.01140243       1.73874     .63684603    -.81034032     .00482489      .0833775     .13671676     -.1358027

{txt}            lns1:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:       id1_1l:
    strength_i~o            d2            d3            d4            d5            d6            d7            d8
y1 {res}    .01856201     3.1536312     2.8400737     2.7247193     4.7403913       2.73437     1.8484163     2.3023262

{txt}          id1_1l:       id1_1l:       id1_1l:       id1_1l:        id1_1:           f1:           f1:           f1:
              d9           d10           d11           d12            d1          race        gender     education
y1 {res}    1.2179759     3.3025811     1.8747595     2.1880662     .30773636     .01075388    -.04578925     .00489744

{txt}              f1:           f1:           f1:           f1:           f1:           f1:
            ideo           pid          risk           nep       economy       network
y1 {res}   -.02300878     .05974959     .54682433      .5493917    -.44322866      .0525385
{reset}
{com}. 
. **********************
. ** PARTISANSHIP MODELS
. **********************
. ** MODEL 6 (partisan conditioning) 
. ** specification of het  
. eq het: education gw_know pluralism educ_plural pid pid_plural
{txt}
{com}. 
. ** estimate structural and variance equation 
. gllamm y, i(id) l(soprob) f(binom) thres(thr) eqs(load) geqs(f1) s(het) from(d) adapt 
{res}
{txt}Running adaptive quadrature
Iteration 0:    log likelihood = {res}-4061.6981
{txt}Iteration 1:    log likelihood = {res}-4061.0341
{txt}Iteration 2:    log likelihood = {res}-4060.0254
{txt}Iteration 3:    log likelihood = {res}-4060.0254


{txt}Adaptive quadrature has converged, running Newton-Raphson
Iteration 0:{col 16}log likelihood = {res}-4060.0254{txt}  
Iteration 1:{col 16}log likelihood = {res}-4060.0254{txt}  (not concave)
Iteration 2:{col 16}log likelihood = {res}-4059.0657{txt}  
Iteration 3:{col 16}log likelihood = {res}-4058.9082{txt}  
Iteration 4:{col 16}log likelihood = {res}-4058.8911{txt}  
Iteration 5:{col 16}log likelihood = {res}-4058.8911{txt}  
{res} 
{txt}number of level 1 units = {res}11355
{txt}number of level 2 units = {res}982
 
{txt}Condition Number = {res}614.44758
 
{txt}gllamm model
{res} 
{txt}log likelihood = {res}-4058.8911
 
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           y{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}d2 {c |}{col 14}{res}{space 2} 1.079328{col 26}{space 2}   .30115{col 37}{space 1}    3.58{col 46}{space 3}0.000{col 54}{space 4} .4890848{col 67}{space 3} 1.669571
{txt}{space 10}d3 {c |}{col 14}{res}{space 2}   1.9549{col 26}{space 2}  .343733{col 37}{space 1}    5.69{col 46}{space 3}0.000{col 54}{space 4} 1.281195{col 67}{space 3} 2.628604
{txt}{space 10}d4 {c |}{col 14}{res}{space 2}-.2392492{col 26}{space 2} .2121919{col 37}{space 1}   -1.13{col 46}{space 3}0.260{col 54}{space 4}-.6551377{col 67}{space 3} .1766393
{txt}{space 10}d5 {c |}{col 14}{res}{space 2} .9357346{col 26}{space 2} .4599663{col 37}{space 1}    2.03{col 46}{space 3}0.042{col 54}{space 4} .0342172{col 67}{space 3} 1.837252
{txt}{space 10}d6 {c |}{col 14}{res}{space 2} .4298891{col 26}{space 2} .2306931{col 37}{space 1}    1.86{col 46}{space 3}0.062{col 54}{space 4}-.0222611{col 67}{space 3} .8820392
{txt}{space 10}d7 {c |}{col 14}{res}{space 2}-.6852222{col 26}{space 2} .1632247{col 37}{space 1}   -4.20{col 46}{space 3}0.000{col 54}{space 4}-1.005137{col 67}{space 3}-.3653077
{txt}{space 10}d8 {c |}{col 14}{res}{space 2} .6568105{col 26}{space 2}  .206564{col 37}{space 1}    3.18{col 46}{space 3}0.001{col 54}{space 4} .2519525{col 67}{space 3} 1.061669
{txt}{space 10}d9 {c |}{col 14}{res}{space 2} .5804525{col 26}{space 2} .1248725{col 37}{space 1}    4.65{col 46}{space 3}0.000{col 54}{space 4} .3357069{col 67}{space 3}  .825198
{txt}{space 9}d10 {c |}{col 14}{res}{space 2}  .028556{col 26}{space 2} .2770423{col 37}{space 1}    0.10{col 46}{space 3}0.918{col 54}{space 4}-.5144369{col 67}{space 3} .5715488
{txt}{space 9}d11 {c |}{col 14}{res}{space 2} 1.738913{col 26}{space 2} .2690323{col 37}{space 1}    6.46{col 46}{space 3}0.000{col 54}{space 4} 1.211619{col 67}{space 3} 2.266207
{txt}{space 9}d12 {c |}{col 14}{res}{space 2} .6510398{col 26}{space 2} .1920756{col 37}{space 1}    3.39{col 46}{space 3}0.001{col 54}{space 4} .2745786{col 67}{space 3} 1.027501
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.7790998{col 26}{space 2}  .151846{col 37}{space 1}   -5.13{col 46}{space 3}0.000{col 54}{space 4}-1.076712{col 67}{space 3}-.4814872
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res} 
{txt}Variance at level 1
------------------------------------------------------------------------------

{res} 
{txt}    equation for log standard deviation: 
{res} 
{txt}    education: {res}.0062679 (.02925851)
{txt}    gw_know: {res}.07653458 (.08236731)
{txt}    pluralism: {res}-.04012485 (.28931011)
{txt}    educ_plural: {res}-.12859214 (.07042074)
{txt}    pid: {res}.03772975 (.0430309)
{txt}    pid_plural: {res}.1209425 (.10488076)
 
{txt}Variances and covariances of random effects
------------------------------------------------------------------------------

{res} 
{txt}***level 2 ({res}id{txt})
{res} 
{txt}    var(1): {res}.10445545 (.04090934)
 
{txt}    loadings for random effect 1
    d1: {res}1 (fixed)
{txt}    d2: {res}2.9461566 (.49967723)
{txt}    d3: {res}2.6172056 (.45401114)
{txt}    d4: {res}2.5919644 (.47308754)
{txt}    d5: {res}4.4729686 (.83075593)
{txt}    d6: {res}2.6317908 (.45503548)
{txt}    d7: {res}1.8181476 (.36748081)
{txt}    d8: {res}2.2082064 (.37774615)
{txt}    d9: {res}1.1752643 (.21656451)
{txt}    d10: {res}3.1838813 (.57181391)
{txt}    d11: {res}1.763353 (.30819789)
{txt}    d12: {res}2.1001661 (.35561065)
 
 
{txt}Regressions of latent variables on covariates
------------------------------------------------------------------------------

{res} 
{txt}    random effect {res}1{txt} has {res}9{txt} covariates:
    race: {res}.01240142 (.03861351)
{txt}    gender: {res}-.04838887 (.02917822)
{txt}    education: {res}.006385 (.01518979)
{txt}    ideo: {res}-.02363594 (.01065511)
{txt}    pid: {res}.0818393 (.03019204)
{txt}    risk: {res}.57693046 (.13532196)
{txt}    nep: {res}.57305692 (.14592362)
{txt}    economy: {res}-.46427848 (.11317615)
{txt}    network: {res}.05545518 (.04611394)
{txt}------------------------------------------------------------------------------

{res} 
{txt}
{com}. matrix f = e(b)
{txt}
{com}. matrix list e(b)
{res}
{txt}e(b)[1,39]
         _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:      _cut11:
             d2           d3           d4           d5           d6           d7           d8           d9
y1 {res}   1.0793279    1.9548998   -.23924922    .93573464    .42988907   -.68522221    .65681051    .58045246

{txt}         _cut11:      _cut11:      _cut11:      _cut11:        lns1:        lns1:        lns1:        lns1:
            d10          d11          d12        _cons    education      gw_know    pluralism  educ_plural
y1 {res}   .02855598     1.738913    .65103984   -.77909985     .0062679    .07653458   -.04012485   -.12859214

{txt}           lns1:        lns1:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:
            pid   pid_plural           d2           d3           d4           d5           d6           d7
y1 {res}   .03772975     .1209425    2.9461566    2.6172056    2.5919644    4.4729686    2.6317908    1.8181476

{txt}         id1_1l:      id1_1l:      id1_1l:      id1_1l:      id1_1l:       id1_1:          f1:          f1:
             d8           d9          d10          d11          d12           d1         race       gender
y1 {res}   2.2082064    1.1752643    3.1838813     1.763353    2.1001661    .32319569    .01240142   -.04838887

{txt}             f1:          f1:          f1:          f1:          f1:          f1:          f1:
      education         ideo          pid         risk          nep      economy      network
y1 {res}     .006385   -.02363594     .0818393    .57693046    .57305692   -.46427848    .05545518
{reset}
{com}. 
. 
{txt}end of do-file

{com}. do "/var/folders/0f/hcwmq_b15hl_dv23cb2m0n2r0000gq/T//SD00733.000000"
{txt}
{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/markramirez/Public/Dropbox/NOAA STUDY KRVZ/Data/BJPS Replication Files/BJPSlogfileBinary.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}24 Jul 2016, 09:46:17
{txt}{.-}
{smcl}
{txt}{sf}{ul off}